Many consumer electronic devices include imaging devices that may attain images or series of images. Such images may be used to perform object detection, object recognition, gesture recognition, or the like. For example, objects may be identified and tracked for focusing the imaging device in image capture settings and recognized for attaching meta data (e.g., providing a name for a recognized face) and a variety of other purposes. Furthermore, gesture recognition may attempt to interpret human gestures typically made via the user's hands or face to provide input to the device for navigating the device, playing games, and so on. Such gesture recognition may allow users to interact with the device naturally and without an intervening mechanical interface such as a keyboard, mouse, or even touch display.
However, detecting, recognizing, and identifying (e.g., labeling) portions of images is difficult and computationally intensive. In particular, identifying or labeling fingers (e.g., accurately labeling a finger as a thumb, index finger, middle finger, ring finger, or little finger) is challenging as the position, orientation, size and shape of the hand may be widely variable within the attained image or images. Furthermore, existing techniques for performing such detection, recognition, and identification of areas of images may be computationally intensive and may consume substantial memory resources.
As such, existing techniques may not provide for robust and fast labeling for fingers of hands or other areas interest of detected objects. Such problems may become critical as the desire to provide object recognition and gesture based interaction for consumer electronic devices in a variety of settings becomes more widespread.